The identification and determination of the main influencing factors of lake salinity play a key role in the control measures and suggestions to prevent lake pollution. To ascertain the salinity status, its evolutionary trend in WL, and its impact on the lake, data describing water quality and the 10 major influencing factors for the period 2003–2012 were employed to analyze the evolutionary process and trend characteristics of the main factors affecting upper irrigation and drainage. The principal component analysis (PCA) method was used to identify the key influencing factors of the salinity of the lake; then, regression analysis was combined with PCA modeling to predict the salinity. Results showed that using a multivariate statistical technique is an effective and objective method for identifying the main influencing factors of salinity in WL, which can provide a scientific basis for the pollution control and environmental management of WL.

湖泊盐度主要影响因素的识别和确定对湖泊污染防治的控制措施和建议具有关键作用。为了解盐度状况、WL 演变趋势及其对湖泊的影响，利用 2003-2012 年描述水质和 10 个主要影响因素的数据，分析了主要影响因素的演变过程和趋势特征。上部排灌。采用主成分分析（PCA）方法识别湖泊盐度的关键影响因素；然后，回归分析与 PCA 模型相结合来预测盐度。结果表明，使用多元统计技术是识别WL盐度主要影响因素的有效且客观的方法，